Predicting grasp inertia with a geometric model

Gideon Kowadlo; Jason Friedman; Tamar Flash

Predicting grasp inertia with a geometric model

Gideon Kowadlo^*, Jason Friedman, Tamar Flash

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Controlling fnger impedance is critical for successful grasping. Understanding how humans achieve this is of great interest for learning about human motor control, as well as for applications in robotic grasping. There have been a number of studies on finger impedance in both the robotics and biological fields. They almost exclusively consider only stiffness and viscosity. However, inertia may play an important role in certain grasps, and is important for calculation of the other impedance properties. This paper reports current progress of a project to create a geometric model of the hand for predicting hand/grasp inertia at different configurations (sensed by a glove that measures joint angles) during a variety of tasks.

Original language	English
Title of host publication	Proceedings of the 2005 Australasian Conference on Robotics and Automation, ACRA 2005
State	Published - 2005
Externally published	Yes
Event	2005 Australian Conference on Robotics and Automation, ACRA 2005 - Sydney, NSW, Australia Duration: 5 Dec 2005 → 7 Dec 2005

Publication series

Name	Proceedings of the 2005 Australasian Conference on Robotics and Automation, ACRA 2005

Conference

Conference	2005 Australian Conference on Robotics and Automation, ACRA 2005
Country/Territory	Australia
City	Sydney, NSW
Period	5/12/05 → 7/12/05

Cite this

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title = "Predicting grasp inertia with a geometric model",

abstract = "Controlling fnger impedance is critical for successful grasping. Understanding how humans achieve this is of great interest for learning about human motor control, as well as for applications in robotic grasping. There have been a number of studies on finger impedance in both the robotics and biological fields. They almost exclusively consider only stiffness and viscosity. However, inertia may play an important role in certain grasps, and is important for calculation of the other impedance properties. This paper reports current progress of a project to create a geometric model of the hand for predicting hand/grasp inertia at different configurations (sensed by a glove that measures joint angles) during a variety of tasks.",

author = "Gideon Kowadlo and Jason Friedman and Tamar Flash",

year = "2005",

language = "אנגלית",

isbn = "0958758379",

series = "Proceedings of the 2005 Australasian Conference on Robotics and Automation, ACRA 2005",

booktitle = "Proceedings of the 2005 Australasian Conference on Robotics and Automation, ACRA 2005",

note = "2005 Australian Conference on Robotics and Automation, ACRA 2005 ; Conference date: 05-12-2005 Through 07-12-2005",

}

Predicting grasp inertia with a geometric model. / Kowadlo, Gideon; Friedman, Jason; Flash, Tamar.
Proceedings of the 2005 Australasian Conference on Robotics and Automation, ACRA 2005. 2005. (Proceedings of the 2005 Australasian Conference on Robotics and Automation, ACRA 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Controlling fnger impedance is critical for successful grasping. Understanding how humans achieve this is of great interest for learning about human motor control, as well as for applications in robotic grasping. There have been a number of studies on finger impedance in both the robotics and biological fields. They almost exclusively consider only stiffness and viscosity. However, inertia may play an important role in certain grasps, and is important for calculation of the other impedance properties. This paper reports current progress of a project to create a geometric model of the hand for predicting hand/grasp inertia at different configurations (sensed by a glove that measures joint angles) during a variety of tasks.

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ER -

Predicting grasp inertia with a geometric model

Abstract

Publication series

Conference

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